Unnamed: 0 int64 0 7.24k | id int64 1 7.28k | raw_text stringlengths 9 124k | vw_text stringlengths 12 15k |
|---|---|---|---|
1,500 | 2,364 | Near-Minimax Optimal Classification with
Dyadic Classification Trees
Clayton Scott
Electrical and Computer Engineering
Rice University
Houston, TX 77005
cscott@rice.edu
Robert Nowak
Electrical and Computer Engineering
University of Wisconsin
Madison, WI 53706
nowak@engr.wisc.edu
Abstract
This paper reports on a fami... | 2364 |@word version:1 achievable:1 polynomial:19 tedious:1 decomposition:2 harder:1 initial:3 cyclic:3 contains:1 fragment:4 configuration:1 series:2 pub:2 nt:1 written:1 dct:10 realistic:1 additive:1 partition:11 enables:3 discrimination:2 leaf:17 selected:1 provides:1 node:30 traverse:1 along:1 constructed:1 c2:5 anc... |
1,501 | 2,365 | Large Scale Online Learning.
L?eon Bottou
NEC Labs America
Princeton NJ 08540
leon@bottou.org
Yann Le Cun
NEC Labs America
Princeton NJ 08540
yann@lecun.com
Abstract
We consider situations where training data is abundant and computing
resources are comparatively scarce. We argue that suitably designed online learnin... | 2365 |@word version:1 inversion:2 achievable:3 suitably:1 disk:1 simulation:1 uncovers:1 covariance:1 tr:4 solid:1 initial:1 chervonenkis:1 outperforms:2 com:1 comparing:1 yet:1 must:5 ronan:1 numerical:2 designed:4 plot:2 update:5 selected:1 device:1 beginning:1 realizing:1 provides:9 coarse:1 org:1 accessed:1 along:1... |
1,502 | 2,366 | Learning a Distance Metric from Relative
Comparisons
Matthew Schultz and Thorsten Joachims
Department of Computer Science
Cornell University
Ithaca, NY 14853
{schultz,tj}@cs.cornell.edu
Abstract
This paper presents a method for learning a distance metric from relative comparison such as ?A is closer to B than A is to... | 2366 |@word cox:2 faculty:9 norm:2 seek:2 decomposition:1 xtest:5 reduction:1 document:11 yet:1 written:3 readily:1 stemming:3 kdd:1 enables:1 plot:4 intelligence:1 selected:1 xk:14 mccallum:2 parametrization:1 ith:1 provides:1 qualitative:6 freitag:1 pairwise:2 roughly:1 clicked:1 becomes:1 webkb:1 notation:1 project:... |
1,503 | 2,367 | Robustness in Markov Decision Problems with
Uncertain Transition Matrices?
Arnab Nilim
Department of EECS ?
University of California
Berkeley, CA 94720
nilim@eecs.berkeley.edu
Laurent El Ghaoui
Department of EECS
University of California
Berkeley, CA 94720
elghaoui@eecs.berkeley.edu
Abstract
Optimal solutions to Mark... | 2367 |@word version:3 polynomial:1 seems:1 seek:2 incurs:1 tr:1 initial:2 contains:3 ours:1 lave:1 yet:1 numerical:1 update:1 stationary:12 accordingly:1 ith:1 short:1 provides:3 finitehorizon:1 expected:4 themselves:1 planning:1 terminal:4 bellman:3 discounted:9 equipped:1 qia:1 provided:1 estimating:1 notation:4 unde... |
1,504 | 2,368 | An Improved Scheme for Detection and
Labelling in Johansson Displays
Claudio Fanti
Marzia Polito
Computational Vision Lab, 136-93
California Institute of Technology
Pasadena, CA 91125, USA
Intel Corporation, SC12-303
2200 Mission College Blvd.
Santa Clara, CA 95054, USA
fanti@vision.caltech.edu
marzia.polito@inte... | 2368 |@word version:4 inversion:1 johansson:4 giudici:1 additively:1 pick:2 accommodate:1 score:6 selecting:2 current:1 com:1 clara:1 must:1 visible:7 realistic:1 partition:2 plot:1 gist:1 occlude:1 greedy:2 fewer:1 plane:1 yi1:1 ith:1 detecting:1 node:3 simpler:1 along:2 ik:1 introduce:2 acquired:1 expected:3 nor:1 mu... |
1,505 | 2,369 | Circuit Optimization Predicts Dynamic
Networks for Chemosensory Orientation in the
Nematode Caenorhabditis elegans
Nathan A. Dunn
John S. Conery
Dept. of Computer Science
University of Oregon
Eugene, OR 97403
{ndunn,conery}@cs.uoregon.edu
Shawn R. Lockery
Institute of Neuroscience
University of Oregon
Eugene, OR 9740... | 2369 |@word version:4 seal:1 grey:1 shading:1 interestingly:1 horvitz:1 current:5 comparing:1 anterior:1 surprising:1 activation:3 intriguing:1 john:1 realistic:1 motor:1 drop:1 nervous:6 ria:1 beginning:2 reciprocal:2 node:2 ron:1 arctan:1 pun:1 along:4 direct:6 awc:3 consists:1 avery:1 pathway:9 behavioral:4 introduc... |
1,506 | 237 | 758
Satyanarayana, Tsividis and Graf
A Reconfigurable Analog VLSI Neural Network
Chip
Srinagesh Satyanarayana and Yannis Tsividis
Department of Electrical Engineering
and
Center for Telecommunications Research
Columbia University, New York, NY 10027, USA
Hans Peter Graf
AT&T
Bell Laboratories
Holmdel, NJ 07733
USA
... | 237 |@word version:1 jlf:1 ttn:1 etann:1 moment:1 electronics:3 configuration:5 contains:1 selecting:1 duong:1 current:15 refresh:9 interrupted:1 ronald:1 shape:1 designed:2 update:5 selected:1 ith:1 short:1 provides:2 constructed:1 differential:6 supply:1 overhead:1 expected:1 simulator:1 actual:2 considering:1 increa... |
1,507 | 2,370 | Automatic Annotation of Everyday Movements
Deva Ramanan and D. A. Forsyth
Computer Science Division
University of California, Berkeley
Berkeley, CA 94720
ramanan@cs.berkeley.edu, daf@cs.berkeley.edu
Abstract
This paper describes a system that can annotate a video sequence with:
a description of the appearance of each... | 2370 |@word version:2 ankle:1 pick:9 tr:1 carry:9 catastrophically:1 initial:1 configuration:17 series:1 fragment:1 selecting:1 contains:1 brien:1 recovered:3 comparing:3 current:1 must:1 visible:1 remove:1 intelligence:1 plane:4 isotropic:1 ith:1 core:1 short:1 record:1 accepting:1 colored:1 detecting:2 provides:1 nod... |
1,508 | 2,371 | Statistical Debugging of Sampled Programs
Alice X. Zheng
EE Division
UC Berkeley
alicez@cs.berkeley.edu
Michael I. Jordan
CS Division and Department of Statistics
UC Berkeley
jordan@cs.berkeley.edu
Ben Liblit
CS Division
UC Berkeley
liblit@cs.berkeley.edu
Alex Aiken
CS Division
UC Berkeley
aiken@cs.berkeley.edu
Ab... | 2371 |@word trial:5 private:1 briefly:1 norm:3 seems:1 nd:1 bn:1 eng:1 elisseeff:1 asks:1 accommodate:1 contains:1 score:13 bc:11 subjective:1 existing:1 scatter:1 must:1 john:1 additive:1 subsequent:1 plot:6 designed:1 aside:1 intelligence:1 leaf:1 guess:2 selected:6 record:4 pointer:5 math:1 location:2 traverse:1 fiv... |
1,509 | 2,372 | Bounded Finite State Controllers
Pascal Poupart
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
ppoupart@cs.toronto.edu
Craig Boutilier
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
cebly@cs.toronto.edu
Abstract
We describe a new approximation algorithm for solving ... | 2372 |@word version:1 compression:2 seek:3 pg:2 initial:6 cyclic:1 exclusively:1 bc:4 interestingly:2 current:7 must:6 readily:1 realize:1 remove:1 designed:1 alone:1 intelligence:3 discovering:1 accordingly:1 hallway:1 steepest:1 meuleau:2 provides:2 characterization:1 node:78 toronto:8 preference:2 zhang:3 redirected... |
1,510 | 2,373 | Minimax embeddings
Matthew Brand
Mitsubishi Electric Research Labs
Cambridge MA 02139 USA
Abstract
Spectral methods for nonlinear dimensionality reduction (NLDR) impose
a neighborhood graph on point data and compute eigenfunctions of a
quadratic form generated from the graph. We introduce a more general
and more robus... | 2373 |@word version:1 compression:1 norm:10 open:1 km:2 cleanly:1 seek:1 mitsubishi:1 tried:1 decomposition:5 asks:1 thereby:1 tr:1 klk:3 shot:2 ld:1 reduction:6 substitution:1 contains:2 eigensolvers:2 series:1 offering:1 recovered:2 z2:1 si:4 must:5 john:1 numerical:8 chicago:1 implying:1 parameterization:6 plane:1 i... |
1,511 | 2,374 | An MCMC-Based Method of Comparing
Connectionist Models in Cognitive Science
Woojae Kim, Daniel J. Navarro?, Mark A. Pitt, In Jae Myung
Department of Psychology
Ohio State University
fkim.1124, navarro.20, pitt.2, myung.1g@osu.edu
Abstract
Despite the popularity of connectionist models in cognitive science,
their perf... | 2374 |@word determinant:1 version:2 proportion:3 nd:6 open:1 simulation:2 covariance:1 unimpressive:1 eld:1 contains:1 daniel:2 interestingly:1 reaction:1 current:2 comparing:3 discretization:1 activation:4 yet:1 must:1 readily:1 analytic:1 v:3 alone:1 half:1 accordingly:1 cult:2 beginning:1 record:1 accepting:1 mental... |
1,512 | 2,375 | Synchrony Detection by Analogue VLSI
Neurons with Bimodal STDP Synapses
Adria Bofill-i-Petit
The University of Edinburgh
Edinburgh, EH9 3JL
Scotland
adria.bofill@ee.ed.ac.uk
Alan F. Murray
The University of Edinburgh
Edinburgh, EH9 3JL
Scotland
alan.murray@ee.ed.ac.uk
Abstract
We present test results from spike-timi... | 2375 |@word inversion:2 stronger:1 seems:1 nd:1 pulse:7 dramatic:1 n8:1 configuration:2 contains:1 efficacy:2 tuned:1 current:8 underly:1 plasticity:9 shape:1 drop:1 aps:1 scotland:2 philipp:1 zhang:1 along:1 constructed:1 direct:1 supply:1 symposium:1 manner:1 introduce:2 p1:2 detects:2 window:15 underlying:1 matched:... |
1,513 | 2,376 | Iterative scaled trust-region learning in
Krylov subspaces via Pearlmutter?s
implicit sparse Hessian-vector multiply
Eiji Mizutani
Department of Computer Science
Tsing Hua University
Hsinchu, 300 TAIWAN R.O.C.
eiji@wayne.cs.nthu.edu.tw
James W. Demmel
Mathematics and Computer Science
University of California at Berke... | 2376 |@word tsing:1 trial:5 exploitation:1 dtk:5 eliminating:1 repository:1 norm:5 proportion:3 version:1 bf:1 nd:1 termination:1 simulation:2 jacob:1 paid:1 reduction:3 initial:1 tuned:1 o2:1 nowlan:1 marquardt:2 yet:2 danny:1 must:3 readily:2 realize:2 numerical:4 partition:1 update:4 a1k:1 alone:4 item:2 steepest:2 ... |
1,514 | 2,377 | Modeling User Rating Profiles For
Collaborative Filtering
Benjamin Marlin
Department of Computer Science
University of Toronto
Toronto, ON, M5S 3H5, CANADA
marlin@cs.toronto.edu
Abstract
In this paper we present a generative latent variable model for
rating-based collaborative filtering called the User Rating Profile... | 2377 |@word version:2 inversion:1 seems:1 proportion:1 stronger:1 open:1 carolina:1 contains:1 score:1 selecting:2 document:1 outperforms:1 z2:1 assigning:1 yet:1 must:1 partition:1 hofmann:2 remove:1 designed:6 update:4 generative:9 intelligence:1 item:24 urp:33 filtered:3 blei:2 provides:1 toronto:3 preference:7 acce... |
1,515 | 2,378 | Policy search by dynamic programming
J. Andrew Bagnell
Carnegie Mellon University
Pittsburgh, PA 15213
Andrew Y. Ng
Stanford University
Stanford, CA 94305
Sham Kakade
University of Pennsylvania
Philadelphia, PA 19104
Jeff Schneider
Carnegie Mellon University
Pittsburgh, PA 15213
Abstract
We consider the policy searc... | 2378 |@word middle:1 version:3 achievable:1 polynomial:7 nd:6 open:3 simulation:1 q1:3 incurs:1 harder:1 reduction:1 initial:4 current:1 tackling:1 must:3 john:2 ronald:1 motor:1 update:4 stationary:23 greedy:1 half:2 intelligence:1 accordingly:1 hallway:4 mccallum:3 completeness:1 provides:3 along:3 constructed:1 diff... |
1,516 | 2,379 | Sparse Representation and Its Applications in
Blind Source Separation
Yuanqing Li, Andrzej Cichocki, Shun-ichi Amari, Sergei Shishkin
RIKEN Brain Science Institute, Saitama, 3510198, Japan
Jianting Cao
Department of Electronic Engineering
Saitama Institute of Technology
Saitama, 3510198, Japan
Fanji Gu
Department of ... | 2379 |@word trial:20 norm:24 nd:1 r:1 simulation:10 decomposition:2 p0:12 solid:1 contains:1 existing:1 recovered:4 bsj:1 si:9 sergei:1 additive:2 n0:2 stationary:1 selected:7 fewer:1 inspection:1 beginning:2 filtered:1 provides:1 node:4 si1:1 five:1 become:2 incorrect:5 prove:2 westerfield:1 introduce:2 ica:4 p1:8 bra... |
1,517 | 238 | 606
Ahmad, Thsauro and He
Asymptotic Convergence of Backpropagation:
Numerical Experiments
Subutai Ahmad
ICSI
1947 Center St.
Berkeley, CA 94704
Gerald Tesauro
mM Watson Labs.
P. O. Box 704
Yorktown Heights, NY
10598
Yu He
Dept. of Physics
Ohio State Univ.
Columbus, OH 43212
ABSTRACT
We have calculated, both ana... | 238 |@word unaltered:1 polynomial:6 nd:1 simulation:4 jacob:2 tr:1 moment:1 initial:2 activation:1 written:1 numerical:14 ctyp:1 shape:1 analytic:4 plot:5 v:4 sudden:1 provides:1 height:1 c2:1 become:3 differential:1 theoretically:1 expected:1 rapid:1 behavior:12 examine:2 decreasing:1 actual:2 becomes:1 what:3 unspeci... |
1,518 | 2,380 | Eye Movements for Reward Maximization
Nathan Sprague
Computer Science Department
University of Rochester
Rochester, NY 14627
sprague@cs.rochester.edu
Dana Ballard
Computer Science Department
University of Rochester
Rochester, NY 14627
dana@cs.rochester.edu
Abstract
Recent eye tracking studies in natural tasks sugges... | 2380 |@word trial:2 version:1 seek:1 propagate:2 simulation:3 pick:1 minus:1 foveal:1 series:1 selecting:3 existing:1 current:2 si:8 must:5 motor:2 update:3 grass:1 intelligence:2 discovering:1 selected:2 item:4 parameterization:1 indicative:1 ith:2 provides:1 coarse:1 location:4 along:2 direct:3 become:1 overhead:1 ra... |
1,519 | 2,381 | A Sampled Texture Prior for Image
Super-Resolution
Lyndsey C. Pickup, Stephen J. Roberts and Andrew Zisserman
Robotics Research Group
Department of Engineering Science
University of Oxford
Parks Road, Oxford, OX1 3PJ
{elle,sjrob,az}@robots.ox.ac.uk
Abstract
Super-resolution aims to produce a high-resolution image fro... | 2381 |@word grey:15 scg:3 tried:1 wexler:1 initial:1 score:1 recovered:3 dx:2 must:1 additive:1 partition:1 analytic:1 plot:3 generative:3 half:1 leaf:1 website:1 intelligence:1 short:1 lr:7 gx:3 mathematical:1 beta:4 lowresolution:3 qualitative:1 huber:23 themselves:1 inspired:1 freeman:1 window:1 begin:1 estimating:1... |
1,520 | 2,382 | ARA*: Anytime A* with Provable Bounds on
Sub-Optimality
Maxim Likhachev, Geoff Gordon and Sebastian Thrun
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
{maxim+, ggordon, thrun}@cs.cmu.edu
Abstract
In real world planning problems, time for deliberation is often limited.
Anytime planners ar... | 2382 |@word interleave:1 open:35 termination:2 grey:4 gradual:1 propagate:2 korf:1 pick:1 minus:1 initial:2 series:7 contains:2 selecting:1 fragment:1 past:1 current:8 comparing:1 si:5 yet:2 must:1 informative:1 remove:3 plot:1 progressively:1 update:4 v:2 greedy:3 fewer:2 intelligence:4 accordingly:1 beginning:1 provi... |
1,521 | 2,383 | A Holistic Approach
to Compositional Semantics:
a connectionist model and robot experiments
Yuuya Sugita
BSI, RIKEN
Hirosawa 2-1, Wako-shi
Saitama 3510198 JAPAN
sugita@bdc.brain.riken.go.jp
Jun Tani
BSI, RIKEN
Hirosawa 2-1, Wako-shi
Saitama 3510198 JAPAN
tani@bdc.brain.riken.go.jp
Abstract
We present a novel connect... | 2383 |@word trial:2 middle:1 seems:1 bptt:2 confirms:1 pold:6 accounting:1 initial:1 cyclic:1 wako:2 current:3 comparing:1 si:12 must:3 john:2 evans:1 realistic:1 enables:1 motor:15 plot:2 designed:1 update:7 intelligence:3 fewer:1 cult:2 deflationary:1 colored:2 filtered:1 node:28 lexicon:4 revisited:1 five:1 ect:3 co... |
1,522 | 2,384 | Increase information transfer rates in BCI
by CSP extension to multi-class
Guido Dornhege1 , Benjamin Blankertz1 , Gabriel Curio2 , Klaus-Robert M?ller1,3
1 Fraunhofer FIRST.IDA, Kekul?str. 7, 12489 Berlin, Germany
2 Neurophysics Group, Dept. of Neurology, Klinikum Benjamin Franklin,
Freie Universit?t Berlin, Hindenbu... | 2384 |@word blankertz1:1 mild:1 cu:2 trial:18 advantageous:1 seems:1 underline:1 nd:1 tedious:1 confirms:1 covariance:5 decomposition:1 eng:8 reduction:1 moment:1 configuration:5 contains:1 tuned:1 interestingly:1 franklin:1 outperforms:1 err:4 current:2 ida:2 comparing:1 analysed:1 activation:2 scatter:3 dx:1 subseque... |
1,523 | 2,385 | Online Classification on a Budget
Koby Crammer
Computer Sci. & Eng.
Hebrew University
Jerusalem 91904, Israel
Jaz Kandola
Royal Holloway,
University of London
Egham, UK
Yoram Singer
Computer Sci. & Eng.
Hebrew University
Jerusalem 91904, Israel
kobics@cs.huji.ac.il
jaz@cs.rhul.ac.uk
singer@cs.huji.ac.il
Abstract... | 2385 |@word version:7 polynomial:1 norm:3 c0:2 eng:2 reduction:2 contains:1 att:1 past:3 current:1 com:1 comparing:1 jaz:2 must:1 john:1 additive:3 predetermined:1 kyb:1 remove:3 designed:2 plot:9 update:6 v:1 fewer:1 devising:1 classier:1 ith:2 provides:1 contribute:2 location:1 firstly:2 simpler:2 mathematical:1 beco... |
1,524 | 2,386 | Using the Forest to See the Trees: A Graphical
Model Relating Features, Objects, and Scenes
Kevin Murphy
MIT AI lab
Cambridge, MA 02139
murphyk@ai.mit.edu
Antonio Torralba
MIT AI lab
Cambridge, MA 02139
torralba@ai.mit.edu
William T. Freeman
MIT AI lab
Cambridge, MA 02139
wtf@ai.mit.edu
Abstract
Standard approaches... | 2386 |@word briefly:1 version:3 seems:1 tried:1 minus:1 shot:1 harder:1 contains:2 score:1 kurt:9 freitas:1 contextual:3 luo:2 yet:1 additive:1 sanjiv:1 partition:1 informative:1 shape:3 remove:1 plot:1 gist:22 v:4 alone:4 half:1 selected:2 fewer:1 greedy:1 intelligence:2 mccallum:1 pisarevsky:1 detecting:5 boosting:15... |
1,525 | 2,387 | PAC-Bayesian Generic Chaining
Jean-Yves Audibert ?
Universit?e Paris 6
Laboratoire de Probabilit?es et Mod`eles al?eatoires
175 rue du Chevaleret
75013 Paris - France
jyaudibe@ccr.jussieu.fr
Olivier Bousquet
Max Planck Institute for Biological Cybernetics
Spemannstrasse 38
D-72076 T?ubingen - Germany
olivier.bousquet@... | 2387 |@word version:3 tedious:1 d2:2 tried:1 contains:3 series:1 chervonenkis:4 existing:1 refines:1 partition:5 kj0:3 assurance:1 successive:3 dn:3 prove:1 combine:7 introduce:4 mpg:1 inspired:1 decreasing:3 actual:1 considering:2 totally:1 provided:2 mek:1 notation:4 bounded:1 moreover:1 developed:1 guarantee:1 pseud... |
1,526 | 2,388 | Learning Spectral Clustering
Francis R. Bach
Computer Science
University of California
Berkeley, CA 94720
fbach@cs.berkeley.edu
Michael I. Jordan
Computer Science and Statistics
University of California
Berkeley, CA 94720
jordan@cs.berkeley.edu
Abstract
Spectral clustering refers to a class of techniques which rely ... | 2388 |@word version:1 norm:4 heuristically:1 simulation:3 decomposition:1 p0:3 tr:8 solid:2 carry:1 configuration:1 selecting:1 existing:1 current:2 comparing:1 cad:1 written:1 john:1 additive:1 partition:20 plot:2 stationary:1 plane:1 short:2 core:1 provides:2 successive:1 mathematical:1 dn:2 inter:1 indeed:1 inspired... |
1,527 | 2,389 | Information Dynamics and Emergent
Computation in Recurrent Circuits of Spiking
Neurons
Thomas Natschl?ager, Wolfgang Maass
Institute for Theoretical Computer Science
Technische Universitaet Graz
A-8010 Graz, Austria
{tnatschl, maass}@igi.tugraz.at
Abstract
We employ an efficient method using Bayesian and linear classi... | 2389 |@word version:3 duda:1 nd:1 bf:4 simulation:2 pulse:1 overwritten:1 methodologically:1 solid:1 carry:1 initial:1 series:1 contains:1 liquid:1 current:15 discretization:1 trustworthy:1 si:10 john:1 subsequent:2 realistic:1 visible:2 numerical:1 fund:1 guess:1 nervous:1 beginning:3 ith:1 short:1 coarse:2 provides:2... |
1,528 | 239 | Digital-Analog Hybrid Synapse Chips for Electronic Neural Networks
Digital-Analog Hybrid Synapse Chips for
Electronic Neural Networks
A Moopenn, T. Duong, and AP. Thakoor
Center for Space Microelectronics Technology
Jet Propulsion Laboratory/California Institute of Technology
Pasadena, CA 91109
ABSTRACf
Cascadable, ... | 239 |@word version:2 downloading:1 usee:1 thereby:1 minus:2 solid:1 etann:1 initial:4 contains:1 suppressing:1 duong:7 current:13 john:1 sponsored:1 selected:1 ith:1 provides:2 quantized:3 along:3 consists:3 symp:1 inter:1 expected:1 behavior:1 multi:1 simulator:1 encouraging:1 pf:1 increasing:2 provided:1 project:1 li... |
1,529 | 2,390 | Learning Near-Pareto-Optimal Conventions in
Polynomial Time
Tuomas Sandholm
CS Department
Carnegie Mellon University
Pittsburgh, PA 15213
sandholm@cs.cmu.edu
Xiaofeng Wang
ECE Department
Carnegie Mellon University
Pittsburgh, PA 15213
xiaofeng@andrew.cmu.edu
Abstract
We study how to learn to play a Pareto-optimal st... | 2390 |@word version:3 polynomial:14 a02:4 willing:1 seek:1 hu:1 q1:1 paid:2 thereby:1 initial:3 contains:1 existing:2 current:1 comparing:1 intriguing:1 attracted:1 designed:1 update:3 hash:7 stationary:5 selected:1 record:1 provides:1 node:1 preference:13 along:2 constructed:1 direct:4 symposium:1 persistent:4 advocat... |
1,530 | 2,391 | Inferring State Sequences for Non-linear
Systems with Embedded Hidden Markov Models
Radford M. Neal, Matthew J. Beal, and Sam T. Roweis
Department of Computer Science
University of Toronto
Toronto, Ontario, Canada M5S 3G3
{radford,beal,roweis}@cs.utoronto.ca
Abstract
We describe a Markov chain method for sampling fro... | 2391 |@word trial:2 middle:1 stronger:1 suitably:2 crucially:1 tried:2 pick:7 tr:2 initial:3 contains:1 current:13 discretization:6 assigning:1 written:2 must:2 realistic:1 utml:1 designed:3 plot:5 update:18 resampling:1 selected:3 leaf:1 plane:2 short:2 toronto:5 five:1 height:1 relabelling:1 constructed:1 excellence:... |
1,531 | 2,392 | Plasticity Kernels and Temporal Statistics
Peter Dayan1 Michael Hausser 2 Michael London1?2
1GCNU, 2WIBR, Dept of Physiology
UCL, Gower Street, London
dayan@gats5y.ucl.ac.uk
{m.hausser,m.london}@ucl.ac.uk
Abstract
Computational mysteries surround the kernels relating the
magnitude and sign of changes in efficacy as ... | 2392 |@word trial:1 version:5 pw:1 hippocampus:2 d2:1 additively:1 seek:1 r:2 solid:2 efficacy:3 score:4 interestingly:1 numerical:1 plasticity:21 wanted:1 remove:2 plot:3 depict:1 aps:1 stationary:1 half:1 implying:1 signalling:1 tdp:10 isotropic:1 short:1 dear:1 accepting:1 filtered:2 provides:1 successive:1 rc:1 mat... |
1,532 | 2,393 | Perspectives on Sparse Bayesian Learning
David Wipf, Jason Palmer, and Bhaskar Rao
Department of Electrical and Computer Engineering
University of California, San Diego, CA 92092
dwipf,japalmer@ucsd.edu, brao@ece.ucsd.edu
Abstract
Recently, relevance vector machines (RVM) have been fashioned from a
sparse Bayesian le... | 2393 |@word duda:1 nd:1 simulation:1 solid:2 accommodate:1 necessity:2 selecting:3 subjective:1 current:1 must:7 readily:1 analytic:2 pertinent:1 remove:1 drop:2 plot:3 sponsored:1 intelligence:1 parameterization:4 location:1 along:5 direct:1 viable:1 combine:1 manner:1 spine:4 nor:1 decreasing:1 actual:1 increasing:1 ... |
1,533 | 2,394 | Maximum Likelihood Estimation of a Stochastic
Integrate-and-Fire Neural Model?
Jonathan W. Pillow, Liam Paninski, and Eero P. Simoncelli
Howard Hughes Medical Institute
Center for Neural Science
New York University
{pillow, liam, eero}@cns.nyu.edu
Abstract
Recent work has examined the estimation of models of stimulus... | 2394 |@word version:3 middle:7 stronger:2 pulse:4 simulation:1 covariance:1 moment:1 contains:1 egt:1 current:17 ka:1 nt:3 yet:1 realistic:2 hyperpolarizing:2 numerical:3 interspike:9 shape:2 plasticity:1 gv:1 plot:1 designed:1 overriding:1 implying:1 fewer:2 parametrization:1 ith:2 filtered:2 leakiness:1 colored:1 cha... |
1,534 | 2,395 | Mechanism of neural interference
by transcranial magnetic stimulation:
network or single neuron?
Yoichi Miyawaki
RIKEN Brain Science Institute
Wako, Saitama 351-0198, JAPAN
yoichi miyawaki@brain.riken.jp
Masato Okada
RIKEN Brain Science Institute
PRESTO, JST
Wako, Saitama 351-0198, JAPAN
okada@brain.riken.jp
Abstrac... | 2395 |@word briefly:1 middle:1 sharpens:1 seems:2 nd:2 pulse:25 tried:1 eng:2 solid:1 reduction:3 initial:5 hereafter:1 mainen:3 wako:2 existing:2 current:12 recovered:1 activation:2 yet:2 must:2 physiol:2 distant:1 happen:1 numerical:1 subsequent:3 shape:1 periodically:1 motor:1 visibility:1 opin:1 accordingly:1 plane... |
1,535 | 2,396 | ICA-Based Clustering of Genes from
Microarray Expression Data
Su-In Lee* and Serafim Batzoglou?
Department of Electrical Engineering
?
Department of Computer Science
Stanford University, Stanford, CA 94305
silee@stanford.edu, serafim@cs.stanford.edu
*
Abstract
We propose an unsupervised methodology using independent
c... | 2396 |@word version:1 polynomial:4 d2:7 serafim:2 versatile:1 reduction:1 liu:1 contains:3 uncovered:1 rkhs:1 interestingly:1 outperforms:1 current:1 comparing:2 activation:1 scatter:3 physiol:1 subsequent:1 realistic:1 remove:1 reproducible:1 hypothesize:1 plot:3 designed:1 selected:1 website:2 inspection:1 xk:3 yi1:1... |
1,536 | 2,397 | Max-Margin Markov Networks
Ben Taskar Carlos Guestrin Daphne Koller
{btaskar,guestrin,koller}@cs.stanford.edu
Stanford University
Abstract
In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs),
which maximize the m... | 2397 |@word faculty:1 version:1 polynomial:5 proportion:1 stronger:1 norm:3 open:1 seek:1 r:1 decomposition:1 dramatic:1 contains:1 selecting:2 document:1 interestingly:1 outperforms:1 existing:1 current:1 assigning:1 yet:1 must:4 dx:1 parsing:1 belmont:1 hofmann:1 analytic:1 update:1 selected:2 website:1 yr:5 mccallum... |
1,537 | 2,398 | Local Phase Coherence
and the Perception of Blur
Zhou Wang and Eero P. Simoncelli
Howard Hughes Medical Institute
Center for Neural Science and Courant Institute of Mathematical Sciences
New York University, New York, NY 10003
zhouwang@ieee.org, eero.simoncelli@nyu.edu
Humans are able to detect blurring of visual ima... | 2398 |@word version:2 compression:2 seems:4 stronger:1 decomposition:1 mammal:2 carry:1 reduction:2 configuration:1 exclusively:1 disparity:2 groundwork:1 past:1 subjective:2 comparing:2 scatter:1 dx:1 written:2 finest:3 must:1 john:1 blur:22 shape:1 webster:2 visibility:1 hypothesize:1 update:1 discrimination:1 half:1... |
1,538 | 2,399 | Optimal Manifold Representation of Data:
An Information Theoretic Approach
Denis Chigirev and William Bialek
Department of Physics and the Lewis-Sigler Institute for Integrative Genomics
Princeton University, Princeton, New Jersey 08544
chigirev,wbialek@princeton.edu
Abstract
We introduce an information theoretic met... | 2399 |@word compression:7 seems:1 integrative:1 willing:2 linearized:1 tried:2 ality:1 accommodate:1 reduction:8 renewed:1 recovered:1 must:2 grassberger:2 shape:5 remove:1 plot:4 v:1 generative:5 fewer:1 plane:5 ith:1 provides:2 characterization:1 node:1 denis:1 simpler:1 mathematical:1 along:2 constructed:1 become:2 ... |
1,539 | 24 | 95
OPTIMAL NEURAL SPIKE CLASSIFICATION
Amir F. Atiya(*) and James M. Bower(**)
(*) Dept. of Electrical Engineering
(**) Division of Biology
California Institute of Technology
Ca 91125
Abstract
Being able to record the electrical activities of a number of neurons simultaneously is likely
to be important in the study o... | 24 |@word duda:1 eng:3 cla:1 mention:1 solid:4 comparing:1 tackling:1 must:1 bd:1 john:1 realistic:1 distant:1 happen:1 shape:14 designed:1 sponsored:1 devising:1 nervous:1 amir:1 beginning:3 record:3 filtered:1 provides:1 detecting:3 height:1 become:1 incorrect:1 inside:1 falsely:2 pairwise:1 inter:2 frequently:1 mult... |
1,540 | 240 | 274
WeinshalI, Edelman and BiiIthofT
A self-organizing multiple-view representation
of 3D objects
Daphna Weinshall
Center for Biological
Information Processing
MIT E25-201
Cambridge, MA 02139
Shimon Edelman
Center for Biological
Information Processing
MIT E25-201
Cambridge, MA 02139
Heinrich H. BiilthofF
Dept. of ... | 240 |@word weins:2 judgement:1 proportion:1 stronger:1 tat:1 simulation:1 diametrically:1 initial:4 tuned:1 subjective:1 activation:7 must:1 readily:1 r1c:1 girosi:2 shape:2 half:2 beginning:1 divita:2 mental:13 location:1 successive:2 five:1 direct:4 become:1 edelman:15 consists:1 oflocally:1 manner:1 behavior:1 thems... |
1,541 | 2,400 | Wormholes Improve Contrastive Divergence
Geoffrey Hinton, Max Welling and Andriy Mnih
Department of Computer Science, University of Toronto
10 King?s College Road, Toronto, M5S 3G5 Canada
{hinton,welling,amnih}@cs.toronto.edu
Abstract
In models that define probabilities via energies, maximum likelihood
learning typic... | 2400 |@word version:1 stronger:1 d2:1 simulation:1 covariance:3 decomposition:1 contrastive:6 tr:1 solid:2 initial:4 contains:1 offering:1 current:1 elliptical:1 comparing:1 dx:1 must:2 realize:1 numerical:2 additive:1 shape:2 update:6 stationary:2 half:1 selected:2 unacceptably:1 isotropic:1 iso:3 steepest:2 provides:... |
1,542 | 2,401 | A probabilistic model of auditory space
representation in the barn owl
Brian J. Fischer
Dept. of Electrical and Systems Eng.
Washington University in St. Louis
St. Louis, MO 63110
fischerb@pcg.wustl.edu
Charles H. Anderson
Department of Anatomy and Neurbiology
Washington University in St. Louis
St. Louis, MO 63110
ch... | 2401 |@word version:3 duda:1 cha:1 simulation:1 eng:1 pressure:2 recursively:1 initial:4 disparity:1 past:2 existing:1 neurophys:1 si:4 dx:1 must:4 olive:1 plot:1 medial:1 alone:3 cue:26 half:1 yr:4 stationary:1 tone:3 plane:1 filtered:2 location:32 mathematical:1 windowed:2 consists:1 interaural:5 pathway:1 manner:1 l... |
1,543 | 2,402 | Towards social robots: Automatic evaluation of
human-robot interaction by face detection and
expression classification
M.S. Bartlett , G. Littlewort
, I. Fasel , J. Chenu
, T. Kanda ,
H. Ishiguro , and J.R. Movellan
Institute for Neural Computation, University of California, San Diego
Inte... | 2402 |@word judgement:1 achievable:1 polynomial:1 open:1 instruction:1 grey:1 dramatic:1 series:1 genetic:1 animated:3 past:1 current:1 comparing:1 reminiscent:1 cottrell:1 additive:1 informative:1 designed:1 joy:5 alone:1 intelligence:1 selected:10 fewer:1 advancement:1 provides:2 boosting:5 location:2 preference:1 al... |
1,544 | 2,403 | Invariant Pattern Recognition
by Semidefinite Programming Machines
Thore Graepel
Microsoft Research Ltd.
Cambridge, UK
thoreg@microsoft.com
Ralf Herbrich
Microsoft Research Ltd.
Cambridge, UK
rherb@microsoft.com
Abstract
Knowledge about local invariances with respect to given pattern
transformations can greatly impr... | 2403 |@word exploitation:1 version:6 polynomial:39 open:1 p0:1 brightness:1 thoreg:1 tr:2 contains:2 com:2 yet:1 scatter:1 written:6 oldenbourg:1 benign:1 plot:3 v:2 half:1 plane:2 herbrich:2 org:1 zhang:1 five:2 combine:1 expected:2 shearing:2 sdp:12 brain:1 actual:1 considering:2 solver:1 increasing:1 provided:1 xx:1... |
1,545 | 2,404 | Approximate Expectation
Tom Heskes, Onno Zoeter, and Wim Wiegerinck
SNN, University of Nijmegen
Geert Grooteplein 21, 6525 EZ, Nijmegen, The Netherlands
Abstract
We discuss the integration of the expectation-maximization (EM) algorithm
for maximum likelihood learning of Bayesian networks with belief propagation
algor... | 2404 |@word version:2 middle:1 grooteplein:1 simulation:4 mitsubishi:1 decomposition:1 covariance:1 paid:1 minus:2 solid:3 kappen:1 moment:3 contains:1 current:2 yet:4 written:1 must:1 subsequent:1 happen:1 partition:1 plot:2 update:2 intelligence:2 generative:1 slowing:1 complication:1 node:6 direct:1 become:1 qij:5 c... |
1,546 | 2,405 | Classification with Hybrid
Generative/Discriminative Models
Rajat Raina, Yirong Shen, Andrew Y. Ng
Computer Science Department
Stanford University
Stanford, CA 94305
Andrew McCallum
Department of Computer Science
University of Massachusetts
Amherst, MA 01003
Abstract
Although discriminatively trained classifiers are ... | 2405 |@word version:4 briefly:1 pick:2 dramatic:1 solid:2 generatively:5 score:1 offering:1 document:27 outperforms:4 lang:1 assigning:1 parsing:1 john:3 stemming:1 subsequent:1 partition:1 christian:2 remove:1 plot:3 v:14 generative:19 indicative:1 mccallum:4 ith:5 contribute:1 ron:1 scholkopf:1 incorrect:1 prove:1 ad... |
1,547 | 2,406 | Identifying Structure across Prepartitioned Data
Ido Dagan
Zvika Marx
Department of CS
Neural Computation Center
Bar-Ilan University
The Hebrew University
Jerusalem, Israel, 91904 Ramat-Gan, Israel, 52900
Eli Shamir
School for CS
The Hebrew University
Jerusalem, Israel, 91904
Abstract
We propose an information-theore... | 2406 |@word eliminating:1 compression:3 seems:1 proportion:5 stronger:1 gradual:1 seek:1 accounting:1 p0:2 paid:1 reduction:1 initial:1 configuration:11 cp2:2 score:4 ours:1 suppressing:2 subjective:1 current:2 od:1 si:1 tackling:1 john:1 partition:21 hofmann:1 designed:1 update:3 v:3 alone:1 implying:1 half:1 cp3:4 in... |
1,548 | 2,407 | Tree-structured approximations by expectation
propagation
Thomas Minka
Department of Statistics
Carnegie Mellon University
Pittsburgh, PA 15213 USA
minka@stat.cmu.edu
Yuan Qi
Media Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139 USA
yuanqi@media.mit.edu
Abstract
Approximation structure plays an ... | 2407 |@word trial:2 version:1 open:1 propagate:5 kappen:2 liu:2 tuned:1 outperforms:1 comparing:2 written:1 must:1 numerical:2 partition:1 remove:1 plot:1 update:2 leaf:1 website:1 xk:17 dissertation:1 multiset:1 node:23 five:1 along:1 yuan:1 fitting:1 pairwise:4 multi:2 inspired:1 freeman:2 decomposed:1 automatically:... |
1,549 | 2,408 | Analytical solution of spike-timing dependent
plasticity based on synaptic biophysics
Bernd Porr, Ausra Saudargiene and Florentin W?org?otter
Computational Neuroscience
Psychology
University of Stirling
FK9 4LR Stirling, UK
{Bernd.Porr,ausra,worgott}@cn.stir.ac.uk
Abstract
Spike timing plasticity (STDP) is a special ... | 2408 |@word middle:1 version:1 longterm:1 inversion:1 seems:2 open:1 propagate:1 postsynaptically:1 solid:2 series:1 efficacy:1 daniel:1 current:13 activation:3 attracted:1 written:1 john:1 physiol:1 realistic:3 plasticity:15 shape:11 designed:2 aps:1 half:1 isotropic:1 realism:1 lr:1 filtered:1 math:1 location:1 org:2... |
1,550 | 2,409 | A Mixed-Signal VLSI for Real-Time
Generation of Edge-Based Image Vectors
Masakazu Yagi, Hideo Yamasaki, and Tadashi Shibata*
Department of Electronic Engineering
*Department of Frontier Informatics
The University of Tokyo
7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan
mgoat@dent.osaka-u.ac.jp, hideo@if.t.u-tokyo.ac.jp... | 2409 |@word kong:1 briefly:1 loading:1 nd:1 simulation:6 paid:1 minus:1 solid:1 carry:2 electronics:1 liu:1 series:1 luo:1 must:2 realize:1 designed:2 succeeding:1 msb:3 beginning:1 detecting:1 firstly:1 five:3 direct:1 differential:1 supply:1 ray:1 symp:1 manner:1 terminal:2 inspired:1 equipped:1 circuit:22 pel:1 deve... |
1,551 | 241 | 266
Zemel, Mozer and Hinton
TRAFFIC: Recognizing Objects Using
Hierarchical Reference Frame Transformations
Richard S. Zemel
Computer Science Dept.
University of Toronto
Toronto, ONT M5S lA4
Michael C. Mozer
Computer Science Dept.
University of Colorado
Boulder, CO 80309-0430
Geoffrey E. Hinton
Computer Science De... | 241 |@word hierachy:1 version:1 simulation:1 thereby:2 tr:2 recursively:1 carry:1 initial:1 configuration:2 contains:3 bc:1 si:3 assigning:1 must:1 written:1 visible:2 realistic:1 shape:3 xif:4 cue:1 selected:1 intelligence:1 detecting:1 toronto:6 successive:1 simpler:1 along:2 burst:1 become:2 manner:3 expected:1 freq... |
1,552 | 2,410 | The IM Algorithm : A variational
approach to Information Maximization
David Barber
Felix Agakov
Institute for Adaptive and Neural Computation : www.anc.ed.ac.uk
Edinburgh University, EH1 2QL, U.K.
Abstract
The maximisation of information transmission over noisy channels
is a common, albeit generally computationally d... | 2410 |@word trial:1 briefly:1 eliminating:1 compression:3 norm:3 middle:1 suitably:2 covariance:5 decomposition:1 tr:3 moment:2 initial:1 multiuser:1 current:1 recovered:1 si:16 yet:1 reminiscent:1 attracted:1 partition:1 wx:1 enables:2 update:1 intelligence:1 guess:1 mpm:1 isotropic:2 ith:1 toronto:1 direct:2 become:1... |
1,553 | 2,411 | On the concentration of expectation and
approximate inference in layered networks
XuanLong Nguyen
University of California
Berkeley, CA 94720
xuanlong@cs.berkeley.edu
Michael I. Jordan
University of California
Berkeley, CA 94720
jordan@cs.berkeley.edu
Abstract
We present an analysis of concentration-of-expectation p... | 2411 |@word exploitation:1 version:1 simulation:3 propagate:2 accounting:1 solid:1 recursively:1 rightmost:2 reassurance:2 varx:1 dashdot:1 partition:1 drop:1 plot:6 update:1 comn:1 pursued:1 intelligence:1 xk:1 ith:1 provides:4 math:1 node:50 diagnosing:1 unbounded:1 mathematical:1 x1l:5 c2:2 viable:2 prove:1 paragrap... |
1,554 | 2,412 | A Biologically Plausible Algorithm
for Reinforcement-shaped
Representational Learning
Maneesh Sahani
W.M. Keck Foundation Center for Integrative Neuroscience
University of California, San Francisco, CA 94143-0732
maneesh@phy.ucsf.edu
Abstract
Significant plasticity in sensory cortical representations can be driven in... | 2412 |@word proceeded:1 version:1 seems:2 distribue:1 c0:7 nd:1 integrative:1 simulation:6 gradual:1 covariance:3 paid:1 solid:1 carry:2 initial:2 phy:1 series:1 exclusively:1 prescriptive:1 current:6 recovered:1 activation:3 si:56 must:4 subsequent:2 numerical:1 plasticity:6 motor:1 designed:1 update:6 alone:4 generat... |
1,555 | 2,413 | A Nonlinear Predictive State Representation
Matthew R. Rudary and Satinder Singh
Computer Science and Engineering
University of Michigan
Ann Arbor, MI 48109
{mrudary,baveja}@umich.edu
Abstract
Predictive state representations (PSRs) use predictions of a set of tests to
represent the state of controlled dynamical syst... | 2413 |@word h:1 version:1 briefly:1 polynomial:4 compression:3 open:1 q1:2 initial:1 series:1 past:1 existing:1 o2:14 current:1 must:8 ronald:1 pertinent:1 succeeding:1 update:11 alone:1 fewer:2 leaf:1 beginning:1 ith:2 core:21 short:2 prove:3 introduce:3 blowup:1 nor:1 inspired:2 pitfall:1 window:2 rivest:9 underlying... |
1,556 | 2,414 | A classification-based cocktail-party
processor
Nicoleta Roman, DeLiang Wang
Department of Computer and Information
Science and Center for Cognitive Science
The Ohio State University
Columbus, OH 43210, USA
{niki,dwang}@cis.ohio-state.edu
Guy J. Brown
Department of Computer Science
University of Sheffield
211 Portobe... | 2414 |@word trial:1 version:1 middle:1 stronger:3 seems:1 itdi:1 hu:1 simulation:1 gradual:1 tidigits:1 solid:2 n8:6 reduction:1 initial:5 configuration:15 contains:3 score:10 liu:1 existing:1 current:1 od:1 hohmann:1 si:1 scatter:1 subsequent:1 plot:1 n0:6 cue:12 half:2 tone:3 plane:5 ith:3 core:1 short:1 rch:1 detect... |
1,557 | 2,415 | A Summating, Exponentially-Decaying CMOS
Synapse for Spiking Neural Systems
Rock Z. Shi1,2 and Timothy Horiuchi1,2,3
Electrical and Computer Engineering Department
2
Institute for Systems Research
3
Neuroscience and Cognitive Science Program
University of Maryland, College Park, MD 20742
rshi@glue.umd.edu,timmer@isr.u... | 2415 |@word pw:1 rising:2 inversion:1 glue:1 pulse:18 solid:1 initial:1 liu:2 mainen:1 tuned:1 current:48 neurophys:1 follower:4 realistic:1 plasticity:3 analytic:1 designed:1 short:3 schaik:1 infrastructure:1 provides:2 i0n:4 burst:1 differential:2 m7:4 symposium:1 consists:1 fitting:1 inter:1 rapid:1 behavior:4 brain... |
1,558 | 2,416 | Large margin classifiers: convex loss, low noise,
and convergence rates
Peter L. Bartlett, Michael I. Jordan and Jon D. McAuliffe
Division of Computer Science and Department of Statistics
University of California, Berkeley
Berkeley, CA 94720
{bartlett,jordan,jon}@stat.berkeley.edu
Abstract
Many classification algorit... | 2416 |@word version:1 stronger:1 norm:3 c0:1 closure:1 prominence:1 biconjugate:1 carry:1 necessity:1 chervonenkis:1 elaborating:1 must:1 fn:1 greedy:1 provides:1 boosting:2 mannor:2 ron:1 simpler:2 zhang:6 dn:1 become:1 x0:4 indeed:1 behavior:3 examine:1 growing:1 little:1 provided:3 moreover:2 bounded:2 agnostic:1 wh... |
1,559 | 2,417 | Learning to Find Pre-Images
G?okhan H. Bak?r, Jason Weston and Bernhard Sch?olkopf
Max Planck Institute for Biological Cybernetics
Spemannstra?e 38, 72076 T?ubingen, Germany
{gb,weston,bs}@tuebingen.mpg.de
Abstract
We consider the problem of reconstructing patterns from a feature map.
Learning algorithms using kernel... | 2417 |@word compression:8 seems:1 lodhi:1 seek:1 decomposition:2 elisseeff:1 harder:1 contains:1 rkhs:5 past:1 psarrou:1 current:1 must:2 written:1 cruz:1 numerical:2 hofmann:1 shape:1 selected:3 eskin:1 constructed:1 sii:1 ucsc:1 symposium:1 introduce:1 x0:13 tagging:3 expected:1 indeed:1 ra:1 mpg:1 multi:1 terminal:1... |
1,560 | 2,418 | Estimating Internal Variables and Parameters of
a Learning Agent by a Particle Filter
Kazuyuki Samejima
Kenji Doya
Department of Computational Neurobiology
ATR Computational Neuroscience laboratories;
?Creating the Brain?, CREST, JST.
?Keihan-na Science City?, Kyoto, 619-0288, Japan
{samejima, doya}@atr.jp
Yasumasa Ue... | 2418 |@word neurophysiology:1 trial:12 r:1 simulation:2 solid:2 recursively:1 initial:7 selecting:1 past:1 freitas:2 current:2 comparing:3 must:1 numerical:2 enables:1 motor:2 plot:1 update:2 selected:3 advancement:1 beginning:1 consists:2 behavioral:6 manner:1 introduce:1 acquired:1 x0:5 notably:1 expected:3 behavior:... |
1,561 | 2,419 | Linear Response for Approximate Inference
Max Welling
Department of Computer Science
University of Toronto
Toronto M5S 3G4 Canada
welling@cs.utoronto.ca
Yee Whye Teh
Computer Science Division
University of California at Berkeley
Berkeley CA94720 USA
ywteh@eecs.berkeley.edu
Abstract
Belief propagation on cyclic graph... | 2419 |@word inversion:1 open:3 covariance:21 decomposition:1 solid:1 kappen:1 cyclic:1 loeliger:1 interestingly:1 numerical:1 distant:1 subsequent:1 partition:3 analytic:1 update:6 intelligence:1 xk:25 lr:19 node:34 toronto:2 firstly:1 along:1 direct:1 become:2 ik:3 prove:2 consists:1 g4:1 x0:3 pairwise:4 indeed:1 free... |
1,562 | 242 | 482
Saba and Keeler
Algorithms/or Better Representation and
Faster Learning in Radial
Basis Function Networks
Avijit Saba 1
James D. Keeler
Microelectronics and Computer Technology corporation
3500 West Balcones Center Drive
Austin, Tx 78759
ABSTRACT
In this paper we present upper bounds for the learning rates for
... | 242 |@word effect:1 build:1 hungarian:1 normalized:6 establish:1 casdagli:2 assigned:1 objective:3 added:1 laboratory:1 receptive:15 simulation:1 fa:5 decomposition:1 attractive:1 during:1 self:1 width:9 euclidian:3 eqns:1 essence:1 defmed:1 gradient:3 distance:3 link:2 initial:1 ao:1 series:1 trying:1 allowable:1 summ... |
1,563 | 2,420 | Gaussian Processes in Reinforcement Learning
Carl Edward Rasmussen and Malte Kuss
Max Planck Institute for Biological Cybernetics
Spemannstra?e 38, 72076 T?ubingen, Germany
carl,malte.kuss @tuebingen.mpg.de
Abstract
We exploit some useful properties of Gaussian process (GP) regression
models for reinforcement lear... | 2420 |@word mild:1 exploitation:2 version:3 illustrating:1 polynomial:2 seems:1 achievable:1 tedious:1 covariance:7 thereby:2 moment:3 initial:2 configuration:1 selecting:1 initialisation:1 precluding:1 current:1 yet:1 dx:5 readily:1 numerical:1 subsequent:1 realistic:1 enables:1 analytic:1 update:3 v:2 greedy:4 intell... |
1,564 | 2,421 | Eigenvoice Speaker Adaptation via Composite
Kernel PCA
James T. Kwok, Brian Mak and Simon Ho
Department of Computer Science
Hong Kong University of Science and Technology
Clear Water Bay, Hong Kong
[jamesk,mak,csho]@cs.ust.hk
Abstract
Eigenvoice speaker adaptation has been shown to be effective when only
a small amoun... | 2421 |@word kong:3 version:2 supervectors:7 d2:3 tidigits:6 covariance:5 reduction:2 initial:2 contains:2 series:1 kcr:2 existing:1 si:27 ust:1 numerical:2 subsequent:1 speakerindependent:1 eleven:2 update:1 rd2:1 selected:1 isotropic:2 ith:2 short:2 provides:1 firstly:1 five:1 mathematical:1 consists:2 expected:1 rapi... |
1,565 | 2,422 | Impact of an Energy Normalization
Transform on the Performance of the
LF-ASD Brain Computer Interface
Zhou Yu1
1
2
Steven G. Mason2
Gary E. Birch1,2
Dept. of Electrical and Computer Engineering
University of British Columbia
2356 Main Mall
Vancouver, B.C. Canada V6T 1Z4
Neil Squire Foundation
220-2250 Boundary Road... | 2422 |@word neurophysiology:1 trial:1 implemented:2 effect:5 normalized:7 nervenkr:1 true:4 sfe:1 hence:1 lpf:4 alternating:1 son:1 filter:3 centered:2 eng:2 sin:3 during:4 noted:1 rhythm:1 separate:1 fc2:2 f1:1 performs:1 past:1 interface:3 passive:2 current:1 comparing:2 around:7 index:2 z3:1 activation:2 insufficien... |
1,566 | 2,423 | Probabilistic Inference in Human Sensorimotor
Processing
Konrad P. Ko? rding ?
Institute of Neurology
UCL London
London WC1N 3BG,UK
konrad@koerding.com
Daniel M. Wolpert ?
Institute of Neurology
UCL London
London WC1N 3BG,UK
wolpert@ion.ucl.ac.uk
Abstract
When we learn a new motor skill, we have to contend with both ... | 2423 |@word neurophysiology:1 trial:30 cox:1 middle:2 briefly:1 seems:1 sensed:9 crucially:1 thereby:1 solid:1 daniel:1 tuned:1 current:3 com:3 must:2 john:1 subsequent:1 visible:1 blur:5 midway:3 motor:4 wanted:1 plot:3 designed:1 half:2 selected:1 plane:1 smith:1 provides:1 location:5 simpler:1 direct:1 combine:4 fit... |
1,567 | 2,424 | Envelope-based Planning in Relational MDPs
Natalia H. Gardiol
MIT AI Lab
Cambridge, MA 02139
nhg@ai.mit.edu
Leslie Pack Kaelbling
MIT AI Lab
Cambridge, MA 02139
lpk@ai.mit.edu
Abstract
A mobile robot acting in the world is faced with a large amount of sensory data and uncertainty in its action outcomes. Indeed, almos... | 2424 |@word trial:1 middle:1 version:1 manageable:1 hu:1 nicholson:1 orf:3 arti:5 initial:24 contains:1 fragment:1 daniel:2 kurt:1 current:5 merrick:1 yet:1 must:6 john:1 numerical:1 realistic:1 designed:2 plot:3 alone:1 intelligence:5 leaf:1 fewer:1 smith:1 colored:1 provides:2 recompute:1 height:23 along:2 enterprise... |
1,568 | 2,425 | Bounded invariance and the formation of
place fields
Reto Wyss and Paul F.M.J. Verschure
Institute of Neuroinformatics
University/ETH Z?
urich
Z?
urich, Switzerland
rwyss,pfmjv@ini.phys.ethz.ch
Abstract
One current explanation of the view independent representation of
space by the place-cells of the hippocampus is th... | 2425 |@word determinant:1 exploitation:1 version:1 hippocampus:8 stronger:1 d2:1 simulation:1 series:1 diagonalized:1 current:2 activation:2 scatter:2 must:1 visible:1 subsequent:1 shape:6 motor:1 plot:2 cue:19 half:1 short:1 provides:1 node:3 location:22 sigmoidal:1 zhang:1 along:7 constructed:2 direct:5 become:1 ik:4... |
1,569 | 2,426 | Bayesian Color Constancy
with Non-Gaussian Models
Charles Rosenberg
Thomas Minka
Alok Ladsariya
Computer Science Department
Carnegie Mellon University
Pittsburgh, PA 15213
Statistics Department
Carnegie Mellon University
Pittsburgh, PA 15213
Computer Science Department
Carnegie Mellon University
Pittsburgh, PA 152... | 2426 |@word determinant:1 version:3 tried:1 rgb:1 pick:1 dramatic:1 incurs:1 brightness:3 configuration:1 contains:1 daniel:1 document:1 franklin:1 outperforms:1 discretization:1 yet:1 dx:1 must:2 remove:3 plot:5 sponsored:1 generative:1 selected:1 yr:5 accordingly:1 provides:1 quantized:2 lx:1 preference:1 unbounded:1... |
1,570 | 2,427 | Bias-Corrected Bootstrap and Model
Uncertainty
Harald Steck?
MIT CSAIL
200 Technology Square
Cambridge, MA 02139
harald@ai.mit.edu
Tommi S. Jaakkola
MIT CSAIL
200 Technology Square
Cambridge, MA 02139
tommi@ai.mit.edu
Abstract
The bootstrap has become a popular method for exploring model
(structure) uncertainty. Our... | 2427 |@word version:1 briefly:1 polynomial:1 steck:2 confirms:1 accounting:1 carry:1 moment:1 contains:2 score:5 genetic:1 bc:9 bootstrapped:1 comparing:1 discretization:3 surprising:1 trustworthy:1 scatter:1 plot:1 resampling:4 half:7 prohibitive:1 greedy:1 vanishing:1 short:1 davison:1 location:1 become:1 symposium:2... |
1,571 | 2,428 | Clustering with the Connectivity Kernel
Bernd Fischer, Volker Roth and Joachim M. Buhmann
Institute of Computational Science
Swiss Federal Institute of Technology Zurich
CH-8092 Zurich, Switzerland
{bernd.fischer, volker.roth,jbuhmann}@inf.ethz.ch
Abstract
Clustering aims at extracting hidden structure in dataset. Wh... | 2428 |@word polynomial:10 duda:1 km:5 harder:1 carry:1 contains:1 katoh:1 must:1 distant:1 partition:10 hofmann:1 designed:2 implying:1 selected:2 leaf:1 coarse:2 math:1 node:1 height:1 along:1 become:2 laub:1 scij:2 symp:1 introduce:1 pairwise:16 brucker:1 growing:1 automatically:1 considering:1 becomes:3 project:1 un... |
1,572 | 2,429 | Variational Linear Response
Manfred Opper(1)
Ole Winther(2)
Neural Computing Research Group, School of Engineering and Applied Science,
Aston University, Birmingham B4 7ET, United Kingdom
(2)
Informatics and Mathematical Modelling, Technical University of Denmark,
R. Petersens Plads, Building 321, DK-2800 Lyngby, Denm... | 2429 |@word illustrating:1 inversion:1 polynomial:1 calculus:1 covariance:7 dramatic:1 outlook:1 kappen:1 contains:1 united:1 bc:2 current:1 si:66 guez:1 attracted:1 written:2 partition:3 informative:1 drop:2 intelligence:2 device:1 manfred:1 lr:7 provides:1 simpler:2 mathematical:1 become:1 hojen:1 maturity:1 shorthan... |
1,573 | 243 | 380
Giles, Sun, Chen, Lee and Chen
HIGHER ORDER RECURRENT NETWORKS
& GRAMMATICAL INFERENCE
C. L. Giles?, G. Z. Sun, H. H. Chen, Y. C. Lee, D. Chen
Department of Physics and Astronomy
and
Institute for Advanced Computer Studies
University of Maryland. College Park. MD 20742
* NEC Research Institute
4 Independence Way.... | 243 |@word version:1 nd:2 awijk:10 simulation:4 fmite:7 tr:1 reduction:1 initial:6 liu:2 contains:1 past:2 current:6 activation:2 si:1 dx:1 must:5 readily:1 numerical:1 nemal:5 remove:1 update:1 fewer:1 devising:1 smith:1 ik:3 prove:1 consists:1 terminal:1 actual:1 little:1 increasing:2 becomes:1 sting:1 bounded:1 what... |
1,574 | 2,430 | Linear Program Approximations for Factored
Continuous-State Markov Decision Processes
Milos Hauskrecht and Branislav Kveton
Department of Computer Science and Intelligent Systems Program
University of Pittsburgh
milos,bkveton @cs.pitt.edu
Abstract
Approximate linear programming (ALP) has emerged recently as one of
t... | 2430 |@word version:1 polynomial:1 norm:1 open:1 gfih:1 simulation:1 decomposition:4 paid:1 tr:1 past:1 existing:4 current:2 discretization:2 cmdp:9 written:1 john:1 subsequent:1 realistic:1 update:2 intelligence:3 provides:1 parameterizations:1 node:3 preference:1 simpler:1 mathematical:1 along:1 direct:1 become:1 bet... |
1,575 | 2,431 | Linear Dependent Dimensionality Reduction
Nathan Srebro
Tommi Jaakkola
Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Cambridge, MA 02139
nati@mit.edu,tommi@ai.mit.edu
Abstract
We formulate linear dimensionality reduction as a semi-parametric estimation problem, enabli... | 2431 |@word compression:1 norm:11 open:2 d2:2 dz1:1 seek:1 simulation:1 decomposition:5 covariance:10 dramatic:1 reduction:4 initial:2 series:1 zij:3 daniel:1 interestingly:1 current:1 z2:3 michal:1 yet:1 must:5 additive:20 happen:2 treating:1 isotropic:3 preference:1 unbounded:2 mathematical:1 along:1 c2:3 differentia... |
1,576 | 2,432 | An MDP-Based Approach to Online
Mechanism Design
David C. Parkes
Division of Engineering and Applied Sciences
Harvard University
parkes@eecs.harvard.edu
Satinder Singh
Computer Science and Engineering
University of Michigan
baveja@umich.edu
Abstract
Online mechanism design (MD) considers the problem of providing ince... | 2432 |@word private:2 longterm:1 polynomial:1 stronger:2 nd:1 seek:1 yet:1 must:8 john:1 enables:1 leaf:1 short:1 parkes:5 pdvcg:6 contribute:1 ron:1 simpler:1 along:1 direct:3 symposium:2 incorrect:2 prove:1 introduce:3 ra:3 expected:31 p1:1 multi:1 discounted:2 decreasing:1 becomes:1 moreover:2 maximizes:2 hindsight:... |
1,577 | 2,433 | Margin Maximizing Loss Functions
Saharon Rosset
Watson Research Center
IBM
Yorktown, NY, 10598
srosset@us.ibm.com
Ji Zhu
Department of Statistics
University of Michigan
Ann Arbor, MI, 48109
jizhu@umich.edu
Trevor Hastie
Department of Statistics
Stanford University
Stanford, CA, 94305
hastie@stat.stanford.edu
Abstra... | 2433 |@word mild:1 version:7 polynomial:2 norm:10 seems:1 nd:3 open:1 seek:1 pick:1 incurs:1 necessity:2 com:1 intriguing:1 must:3 additive:1 plane:10 cult:1 vanishing:1 gure:1 provides:2 boosting:21 along:1 ik:3 prove:3 consists:1 theoretically:1 notably:1 multi:14 decreasing:2 considering:1 increasing:2 provided:1 ma... |
1,578 | 2,434 | Semidefinite Programming
by Perceptron Learning
Ralf Herbrich
Thore Graepel
Microsoft Research Ltd., Cambridge, UK
{thoreg,rherb}@microsoft.com
Andriy Kharechko
John Shawe-Taylor
Royal Holloway, University of London, UK
{ak03r,jst}@ecs.soton.ac.uk
Abstract
We present a modified version of the perceptron learning algo... | 2434 |@word msr:1 version:1 polynomial:9 duda:1 c0:17 decomposition:2 thoreg:1 tr:1 necessity:1 initial:3 series:1 efficacy:1 current:2 com:1 surprising:1 toh:1 written:1 must:1 john:2 fn:1 numerical:1 happen:1 plot:2 update:6 prohibitive:1 short:1 herbrich:3 pun:1 mathematical:1 direct:1 symposium:1 prove:1 combine:2 ... |
1,579 | 2,435 | Efficient Multiscale Sampling from
Products of Gaussian Mixtures
Alexander T. Ihler, Erik B. Sudderth, William T. Freeman, and Alan S. Willsky
Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
ihler@mit.edu, esuddert@mit.edu, billf@ai.mit.edu, willsky@mit.edu
Abstract
The ... | 2435 |@word seems:1 termination:1 simulation:1 covariance:1 contrastive:1 thereby:1 solid:1 recursively:1 ld:9 liu:1 series:1 selecting:1 existing:5 freitas:1 current:5 comparing:1 finest:1 readily:1 partition:8 plot:2 resampling:1 half:1 leaf:3 isard:1 ith:2 esuddert:1 farther:3 coarse:3 node:7 location:5 lx:2 five:1 ... |
1,580 | 2,436 | One microphone blind dereverberation
based on quasi-periodicity of speech signals
Tomohiro Nakatani, Masato Miyoshi, and Keisuke Kinoshita
Speech Open Lab., NTT Communication Science Labs., NTT Corporation
2-4, Hikaridai, Seika-cho, Soraku-gun, Kyoto, Japan
{nak,miyo,kinoshita}@cslab.kecl.ntt.co.jp
Abstract
Speech de... | 2436 |@word version:1 briefly:1 open:1 r:5 solid:1 reduction:1 contains:2 mmse:12 existing:1 must:1 designed:1 n0:12 fewer:1 keisuke:1 provides:1 detecting:1 constructed:1 direct:6 become:2 introduce:2 expected:5 ica:1 seika:1 decreasing:1 window:2 becomes:6 provided:2 estimating:1 step2:1 minimizes:2 degrading:1 trans... |
1,581 | 2,437 | Applying Metric-Trees to Belief-Point POMDPs
Joelle Pineau, Geoffrey Gordon
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213
{jpineau,ggordon}@cs.cmu.edu
Sebastian Thrun
Computer Science Department
Stanford University
Stanford, CA 94305
thrun@stanford.edu
Abstract
Recent developments in gri... | 2437 |@word kong:1 compression:2 norm:2 proportion:1 open:1 seek:1 tried:1 pick:1 tr:1 recursively:3 carry:1 initial:1 series:1 selecting:2 tuned:1 current:9 comparing:1 surprising:1 must:5 reminiscent:1 shape:3 update:8 n0:2 newest:1 intelligence:5 selected:3 leaf:2 plane:2 hallway:1 provides:1 consulting:1 node:25 tr... |
1,582 | 2,438 | Semidefinite relaxations for approximate
inference on graphs with cycles
Martin J. Wainwright
Electrical Engineering and Computer Science
UC Berkeley, Berkeley, CA 94720
wainwrig@eecs.berkeley.edu
Michael I. Jordan
Computer Science and Statistics
UC Berkeley, Berkeley, CA 94720
jordan@cs.berkeley.edu
Abstract
We pre... | 2438 |@word trial:7 determinant:18 polynomial:1 suitably:1 open:1 mitsubishi:1 covariance:2 thereby:1 ld:6 moment:4 configuration:1 exclusively:1 outperforms:1 wainwrig:1 must:2 mst:1 partition:7 weyl:1 intelligence:1 accordingly:1 provides:2 characterization:1 complication:1 node:6 mathematical:1 differential:4 prove:... |
1,583 | 2,439 | Learning Bounds for a Generalized Family of
Bayesian Posterior Distributions
Tong Zhang
IBM T.J. Watson Research Center
Yorktown Heights, NY 10598
tzhang@watson.ibm.com
Abstract
In this paper we obtain convergence bounds for the concentration of
Bayesian posterior distributions (around the true distribution) using a
... | 2439 |@word version:2 briefly:1 stronger:1 sex:1 pick:3 mention:2 boundedness:1 com:1 wd:6 surprising:3 guess:1 provides:1 ron:2 lx:6 zhang:2 height:1 direct:1 become:1 introduce:3 hellinger:2 expected:3 behavior:10 globally:1 decreasing:1 estimating:1 underlying:5 bounded:1 notation:3 mass:4 moreover:2 what:1 minimize... |
1,584 | 244 | A Neural Network for Feature Extraction
A Neural Network for Feature Extraction
Nathan Intrator
Div. of Applied Mathematics, and
Center for Neural Science
Brown University
Providence, RI 02912
ABSTRACT
The paper suggests a statistical framework for the parameter estimation problem associated with unsupervised learni... | 244 |@word cox:2 version:1 eliminating:1 polynomial:2 norm:2 retraining:1 simulation:3 seek:2 moment:1 reduction:3 initial:1 past:2 analysed:1 discovering:1 beginning:1 short:1 dissertation:2 detecting:2 math:1 node:5 location:2 sigmoidal:1 simpler:1 rnt:1 become:4 differential:2 multimodality:2 introduce:1 inter:2 hub... |
1,585 | 2,440 | Online Learning of Non-stationary Sequences
Claire Monteleoni and Tommi Jaakkola
MIT Computer Science and Artificial Intelligence Laboratory
200 Technology Square
Cambridge, MA 02139
{cmontel,tommi}@ai.mit.edu
Abstract
We consider an online learning scenario in which the learner can make
predictions on the basis of a... | 2440 |@word version:1 stronger:1 open:1 rigged:1 d2:2 trofimov:1 eng:1 q1:5 initial:1 past:1 existing:3 current:1 discretization:15 comparing:1 yet:3 must:1 subsequent:1 partition:1 update:2 stationary:1 intelligence:2 implying:1 instantiate:1 warmuth:4 ith:1 node:12 location:2 successive:1 preference:1 along:3 c2:1 sy... |
1,586 | 2,441 | The doubly balanced network of spiking
neurons: a memory model with high
capacity
Yuval Aviel*
Interdisciplinary Center for Neural Computation
Hebrew University
Jerusalem, Israel 91904
aviel@cc.huji.ac.il
David Horn
School of Physics
Tel Aviv University
Tel Aviv, Israel 69978
horn@post.tau.ac.il
Moshe Abeles
Interdis... | 2441 |@word complying:1 seems:1 termination:1 simulation:6 tried:2 bn:1 solid:1 past:1 existing:1 current:4 surprising:1 yet:1 dx:1 realize:2 enables:3 plot:3 affair:1 rc:4 along:1 constructed:1 become:1 qualitative:1 doubly:3 sustained:4 eleventh:1 indeed:1 behavior:1 globally:1 pf:1 increasing:1 provided:1 circuit:1 ... |
1,587 | 2,442 | Efficient and Robust Feature Extraction by
Maximum Margin Criterion
Haifeng Li
Tao Jiang
Department of Computer Science
University of California
Riverside, CA 92521
{hli,jiang}@cs.ucr.edu
Keshu Zhang
Department of Electrical Engineering
University of New Orleans
New Orleans, LA 70148
kzhang1@uno.edu
Abstract
A new f... | 2442 |@word nd:2 sammon:1 hu:1 tried:2 covariance:1 tr:24 reduction:6 liu:2 series:1 contains:2 past:1 wd:1 si:17 scatter:27 yet:1 must:2 john:1 shape:1 drop:2 stationary:1 intelligence:1 guess:1 plane:2 simpler:1 zhang:1 consists:1 umbach:1 introduce:1 pairwise:2 theoretically:1 peng:1 multi:1 considering:2 xx:9 maxim... |
1,588 | 2,443 | Non-linear CCA and PCA
by Alignment of Local Models
Jakob J. Verbeek? , Sam T. Roweis? , and Nikos Vlassis?
?
Informatics Institute, University of Amsterdam
?
Department of Computer Science,University of Toronto
Abstract
We propose a non-linear Canonical Correlation Analysis (CCA) method
which works by coordinating o... | 2443 |@word middle:1 version:1 compression:1 ruhr:1 covariance:4 tr:2 reduction:4 contains:1 ours:1 rightmost:1 comparing:1 skipping:1 written:1 readily:1 additive:1 numerical:2 enables:1 plot:1 treating:1 pursued:1 generative:2 half:8 qnt:3 affair:1 provides:3 toronto:1 simpler:1 along:1 transl:1 direct:1 shorthand:1 ... |
1,589 | 2,444 | Laplace Propagation
Alex J. Smola, S.V.N. Vishwanathan
Machine Learning Group
ANU and National ICT Australia
Canberra, ACT, 0200
{smola, vishy}@axiom.anu.edu.au
Eleazar Eskin
Department of Computer Science
Hebrew University Jerusalem
Jerusalem, Israel, 91904
eeskin@cs.columbia.edu
Abstract
We present a novel method ... | 2444 |@word msr:1 briefly:1 repository:2 version:2 advantageous:1 seems:2 r13:1 c0:1 tedious:1 thereby:1 tr:1 carry:1 moment:1 substitution:1 contains:1 current:1 z2:1 skipping:1 si:13 yet:1 written:2 subsequent:1 partition:1 cheap:1 seeding:1 drop:2 update:9 eskin:1 provides:1 recompute:1 ttrain:1 org:1 differential:1... |
1,590 | 2,445 | Gene Expression Clustering with Functional
Mixture Models
Darya Chudova,
Department of Computer Science
University of California, Irvine
Irvine CA 92697-3425
dchudova@ics.uci.edu
Christopher Hart
Division of Biology
California Institute of Technology
Pasadena, CA 91125
hart@caltech.edu
Eric Mjolsness
Department of Co... | 2445 |@word briefly:1 polynomial:1 covariance:3 simplifying:1 bolouri:1 harder:1 initial:4 series:2 score:6 contains:1 affymetrix:2 reaction:1 current:1 surprising:1 scatter:1 additive:1 plot:2 designed:1 generative:3 intelligence:1 short:4 location:1 obser:1 five:1 along:5 differential:3 incorrect:1 consists:1 introdu... |
1,591 | 2,446 | A Neuromorphic Multi-chip Model of a Disparity
Selective Complex Cell
Eric K. C. Tsang and Bertram E. Shi
Dept. of Electrical and Electronic Engineering
Hong Kong University of Science and Technology
Kowloon, HONG KONG SAR
{eeeric,eebert}@ust.hk
Abstract
The relative depth of objects causes small shifts in the left an... | 2446 |@word kong:3 illustrating:1 trotter:1 pulse:3 paid:1 solid:2 initial:1 series:4 disparity:65 contains:1 tuned:39 current:1 ust:1 physiol:1 numerical:1 happen:1 shape:1 enables:1 designed:1 update:1 discrimination:2 v:1 cue:1 half:10 device:1 selected:2 supplying:1 location:10 preference:1 height:1 alert:1 constru... |
1,592 | 2,447 | Ranking on Data Manifolds
Dengyong Zhou, Jason Weston, Arthur Gretton,
Olivier Bousquet, and Bernhard Sch?olkopf
Max Planck Institute for Biological Cybernetics, 72076 Tuebingen, Germany
{firstname.secondname }@tuebingen.mpg.de
Abstract
The Google search engine has enjoyed huge success with its web page
ranking algori... | 2447 |@word trial:1 version:2 duda:1 nd:1 d2:1 reduction:2 initial:2 contains:1 score:24 document:6 outperforms:1 existing:1 comparing:1 skipping:1 scatter:1 john:1 fn:1 periodically:1 happen:1 shape:2 designed:1 plot:2 stationary:5 fewer:1 selected:1 accordingly:1 core:1 provides:1 successive:1 along:2 constructed:2 p... |
1,593 | 2,448 | Distributed Optimization in Adaptive Networks
Ciamac C. Moallemi
Electrical Engineering
Stanford University
Stanford, CA 94305
ciamac@stanford.edu
Benjamin Van Roy
Management Science and Engineering
and Electrical Engineering
Stanford University
Stanford, CA 94305
bvr@stanford.edu
Abstract
We develop a protocol for ... | 2448 |@word longterm:1 termination:2 simulation:2 boundedness:1 initial:1 contains:1 com:1 must:2 numerical:5 partition:1 j1:2 update:6 intelligence:2 leaf:1 device:6 ith:12 provides:2 iterates:1 node:5 relayed:1 mathematical:1 along:2 admission:1 differential:3 supply:1 shorthand:1 pairwise:1 expected:1 indeed:1 rough... |
1,594 | 2,449 | Training fMRI Classifiers to Discriminate
Cognitive States across Multiple Subjects
Xuerui Wang, Rebecca Hutchinson, and Tom M. Mitchell
Center for Automated Learning and Discovery
Carnegie Mellon University
5000 Forbes Avenue, Pittsburgh, PA 15213
{xuerui.wang, rebecca.hutchinson, tom.mitchell}@cs.cmu.edu
Abstract
W... | 2449 |@word trial:4 briefly:1 instruction:1 seek:1 reduction:2 contains:1 bc:1 rightmost:1 activation:5 yet:1 shape:3 haxby:2 designed:1 atlas:1 generative:1 half:1 device:1 mental:1 provides:1 opercularis:2 location:1 five:3 registering:1 along:1 direct:1 midnight:2 symposium:1 forgetting:1 indeed:1 expected:2 behavio... |
1,595 | 245 | Analog Circuits for Constrained Optimization
A nalog Circuits for Constrained Optimization
John C. Platt 1
Computer Science Department, 256-80
California Institute of Technology
Pasadena, CA 91125
ABSTRACT
This paper explores whether analog circuitry can adequately perform constrained optimization. Constrained optim... | 245 |@word build:1 implemented:2 murray:1 multiplier:9 suddenly:1 advantageous:1 seems:1 adequately:1 suitably:1 move:1 capacitance:1 spike:1 fulfillment:3 transient:1 bistable:1 gindi:3 gradient:4 implementing:1 solid:2 barr:1 oa:1 digitization:1 gg:1 mina:1 manifold:2 performs:2 current:2 code:1 around:1 considered:1... |
1,596 | 2,450 | 1-norm Support Vector Machines
Ji Zhu, Saharon Rosset, Trevor Hastie, Rob Tibshirani
Department of Statistics
Stanford University
Stanford, CA 94305
{jzhu,saharon,hastie,tibs}@stat.stanford.edu
Abstract
The standard 2-norm SVM is known for its good performance in twoclass classi?cation. In this paper, we consider the... | 2450 |@word mild:2 version:1 middle:2 norm:50 nd:5 tamayo:2 simulation:8 myeloid:1 solid:1 harder:1 reduction:1 initial:3 selecting:1 bradley:1 current:1 numerical:2 remove:1 update:2 leaf:1 nq:4 boosting:2 zhang:1 downing:1 along:1 ect:1 consists:1 indeed:1 nor:1 automatically:2 little:2 curse:1 becomes:3 linearity:2 ... |
1,597 | 2,451 | An Infinity-sample Theory for Multi-category
Large Margin Classification
Tong Zhang
IBM T.J. Watson Research Center
Yorktown Heights, NY 10598
tzhang@watson.ibm.com
Abstract
The purpose of this paper is to investigate infinity-sample properties of
risk minimization based multi-category classification methods. These
m... | 2451 |@word version:1 seems:1 seek:1 p0:1 pick:1 score:1 current:1 com:1 must:2 written:2 additive:1 numerical:1 implying:2 greedy:1 fewer:1 provides:1 mannor:1 boosting:4 ron:2 lx:2 simpler:1 zhang:3 height:1 c2:1 direct:3 become:1 incorrect:1 prove:2 manner:1 introduce:1 behavior:3 p1:2 multi:18 little:1 increasing:1... |
1,598 | 2,452 | Learning a world model and planning with a
self-organizing, dynamic neural system
Marc Toussaint
Institut f?ur Neuroinformatik
Ruhr-Universit?at Bochum, ND 04
44780 Bochum?Germany
mt@neuroinformatik.rub.de
Abstract
We present a connectionist architecture that can learn a model of the
relations between perceptions and... | 2452 |@word version:1 briefly:1 selforganization:1 nd:1 ruhr:1 simulation:3 seek:1 accounting:1 thereby:2 tr:1 bourgine:1 reynolds:1 existing:4 ka:14 current:11 comparing:2 activation:10 si:2 yet:1 deposited:1 realistic:1 plasticity:4 motor:31 designed:1 update:1 stationary:3 greedy:2 nervous:1 reciprocal:1 core:1 meul... |
1,599 | 2,453 | Can We Learn to Beat the Best Stock
Allan Borodin1 Ran El-Yaniv2 Vincent Gogan1
Department of Computer Science
University of Toronto1 Technion - Israel Institute of Technology2
{bor,vincent}@cs.toronto.edu rani@cs.technion.ac.il
Abstract
A novel algorithm for actively trading stocks is presented. While traditional uni... | 2453 |@word illustrating:1 middle:1 rani:2 polynomial:3 seems:1 proportion:1 compression:3 eliminating:1 nd:1 version:1 achievable:1 leighton:1 bn:3 harder:1 initial:1 contains:1 series:1 liquid:1 outperforms:1 langdon:2 current:3 surprising:1 universality:2 yet:1 must:1 john:2 additive:1 designed:1 update:3 depict:1 v... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.